Watermarking
A Watermark for Large Language Models
* John Kirchenbauer and Jonas Geiping contributed equally.
First arXiv submission: · ICML 2023
Published version: July 2023. The publication and preprint records below retain their own dates and identifiers.
Embeds a statistical watermark by softly favoring a pseudorandom set of tokens during generation. It develops a detection test that does not require the language model and analyzes how the token distribution affects detection strength.
Relationship to Neural Linguistic Steganography
Its related-work section cites Neural Linguistic Steganography among earlier neural information-hiding methods. Its provenance-detection objective differs from concealing the existence of an arbitrary message.
Scope and limitations
Low-entropy text offers less freedom to embed a detectable signal without affecting quality; detection and robustness depend on text length, entropy, and adversarial modifications.
Cite this paper
Published version
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein. A Watermark for Large Language Models. Proceedings of the 40th International Conference on Machine Learning, 2023
Published version BibTeX · Published version CSL-JSON
Show BibTeX
@inproceedings{llm-watermark-publication,
title = {{A Watermark for Large Language Models}},
author = {Kirchenbauer, John and Geiping, Jonas and Wen, Yuxin and Katz, Jonathan and Miers, Ian and Goldstein, Tom},
year = {2023},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
pages = {17061--17084},
volume = {202},
publisher = {PMLR},
url = {https://proceedings.mlr.press/v202/kirchenbauer23a.html}
}
arXiv preprint
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein. A Watermark for Large Language Models. arXiv:2301.10226. First submission: January 24, 2023.
This citation records the first arXiv version and its original title. The version-specific link distinguishes it from later revisions; cite the version supporting your claim.
arXiv preprint BibTeX · arXiv preprint CSL-JSON
Show BibTeX
@misc{llm-watermark-preprint,
title = {{A Watermark for Large Language Models}},
author = {Kirchenbauer, John and Geiping, Jonas and Wen, Yuxin and Katz, Jonathan and Miers, Ian and Goldstein, Tom},
year = {2023},
publisher = {arXiv},
eprint = {2301.10226v1},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2301.10226v1}
}
Primary sources
- Full authors, ICML venue, July 23–29 2023, PMLR volume and pages; no publication DOI listed.
- First submission January 24, 2023; later revised versions distinguished.
- Title-page equal contributions/code; sections 1.2–5 cover entropy and robustness; related work cites Ziegler et al. 2019.
Read or reuse this entry
JSON record · Markdown · Find in the full list
These notes are an editorial reading aid. Consult the primary sources for the authors’ claims, assumptions, and experiments.